title: Automated MRI segmentation for individualized modeling of current flow in the human head.
abstract: OBJECTIVE
  High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of the brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images requires labor-intensive manual segmentation, even when utilizing available automated segmentation tools. Also, accurate placement of many high-density electrodes on an individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process.
  
  
  METHODS
  A fully automated segmentation technique based on Statical Parametric Mapping 8, including an improved tissue probability map and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on four healthy subjects and seven stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets.
  
  
  RESULTS
  The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly.
  
  
  CONCLUSIONS
  Fully automated individualized modeling may now be feasible for large-sample EEG research studies and tDCS clinical trials.
authors.affiliation: Department of Biomedical Engineering, City College of the City University of New York, New York, NY, USA, 10031.
authors.forename: Yu
authors.initials: Y
authors.lastname: Huang
authors.forename: Jacek P
authors.initials: JP
authors.lastname: Dmochowski
authors.forename: Yuzhuo
authors.initials: Y
authors.lastname: Su
authors.forename: Abhishek
authors.initials: A
authors.lastname: Datta
authors.forename: Christopher
authors.initials: C
authors.lastname: Rorden
authors.forename: Lucas C
authors.initials: LC
authors.lastname: Parra
mesh_terms: D000328:Adult; D000368:Aged; D001331:Automation; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006257:Head / Q000033:anatomy & histology* / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008953:Models, Anatomic